Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/148676
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dc.contributor.authorZhang, Hainingen_US
dc.contributor.authorMoon, Seung Kien_US
dc.contributor.authorNgo, Teck Huien_US
dc.date.accessioned2021-05-04T07:07:53Z-
dc.date.available2021-05-04T07:07:53Z-
dc.date.issued2019-
dc.identifier.citationZhang, H., Moon, S. K. & Ngo, T. H. (2019). Hybrid machine learning method to determine the optimal operating process window in aerosol jet 3D printing. ACS Applied Materials & Interfaces, 11(19), 17994-18003. https://dx.doi.org/10.1021/acsami.9b02898en_US
dc.identifier.issn1944-8244en_US
dc.identifier.other0000-0003-2132-8091-
dc.identifier.other0000-0002-2249-7500-
dc.identifier.urihttps://hdl.handle.net/10356/148676-
dc.description.abstractAerosol jet printing (AJP) is a three-dimensional (3D) noncontact and direct printing technology for fabricating customized microelectronic devices on flexible substrates. Despite the capability of fine feature deposition, the complicated relationship between the main process parameters will affect the printing quality significantly in a design space. In this paper, a novel hybrid machine learning method is proposed to determine the optimal operating process window for the AJP process in various design spaces. The proposed method consists of classic machine learning methods, including experimental sampling, data clustering, classification, and knowledge transfer. In the proposed method, a two-dimensional design space is fully explored by a Latin hypercube sampling experimental design at a certain print speed. Then, the influence of the sheath gas flow rate (SHGFR) and the carrier gas flow rate (CGFR) on the printed line quality is analyzed by a K-means clustering approach, and an optimal operating process window is determined by a support vector machine. To efficiently identify more operating process windows at different print speeds, a transfer learning approach is applied to exploit relatedness between different operating process windows. Hence, at a new print speed, the number of line samples for identifying a new operating process window is greatly reduced. Finally, to balance the complex relationship among SHGFR, CGFR, and print speed, a 3D operating process window is determined by an incremental classification approach. Different from experiment-based approaches adopted in 3D printing technologies for quality optimization, the proposed method is developed based on the theory of knowledge discovery and data mining. Therefore, the knowledge in different design spaces can be fully explored and transferred for printed line quality optimization. Moreover, the data-driven-based characteristics can help the proposed method develop a guideline for quality optimization in other 3D printing technologies.en_US
dc.description.sponsorshipNanyang Technological Universityen_US
dc.description.sponsorshipNational Research Foundation (NRF)en_US
dc.language.isoenen_US
dc.relation.ispartofACS Applied Materials & Interfacesen_US
dc.rightsThis document is the Accepted Manuscript version of a Published Work that appeared in final form in ACS Applied Materials & Interfaces, copyright © American Chemical Society after peer review and technical editing by the publisher. To access the final edited and published work see https://doi.org/10.1021/acsami.9b02898.en_US
dc.subjectEngineering::Mechanical engineeringen_US
dc.titleHybrid machine learning method to determine the optimal operating process window in aerosol jet 3D printingen_US
dc.typeJournal Articleen
dc.contributor.schoolSchool of Mechanical and Aerospace Engineeringen_US
dc.identifier.doi10.1021/acsami.9b02898-
dc.description.versionAccepted versionen_US
dc.identifier.pmid31012300-
dc.identifier.scopus2-s2.0-85065755987-
dc.identifier.issue19en_US
dc.identifier.volume11en_US
dc.identifier.spage17994en_US
dc.identifier.epage18003en_US
dc.subject.keywordsAerosol Jet Printingen_US
dc.subject.keywordsDirect Writingen_US
dc.description.acknowledgementThis research work was conducted in the SMRT-NTU Smart Urban Rail Corporate Laboratory with funding support from the National Research Foundation (NRF), SMRT, and Nanyang Technological University under the Corp Lab@ University Scheme.en_US
item.grantfulltextopen-
item.fulltextWith Fulltext-
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